Fault diagnosis and isolation in aircraft gas turbine engines

This paper formulates and validates a novel methodology for diagnosis and isolation of incipient faults in aircraft gas turbine engines. In addition to abrupt large faults, the proposed method is capable of detecting and isolating slowly evolving anomalies (i.e., deviations from the nominal behavior), based on analysis of time series data observed from the instrumentation in engine components. The fault diagnosis and isolation (FDT) algorithm is based upon Symbolic Dynamic Filtering (SDF) that has been recently reported in literature and relies on the principles of Symbolic Dynamics, Statistical Pattern Recognition and Information Theory. Validation of the concept is presented and a real life software architecture is proposed based on the simulation model of a generic two-spool turbofan engine for diagnosis and isolation of incipient faults.

[1]  Yixin Diao,et al.  Stable fault-tolerant adaptive fuzzy/neural control for a turbine engine , 2001, IEEE Trans. Control. Syst. Technol..

[2]  Douglas Lind,et al.  An Introduction to Symbolic Dynamics and Coding , 1995 .

[3]  Asok Ray,et al.  Symbolic dynamic analysis of complex systems for anomaly detection , 2004, Signal Process..

[4]  Asok Ray,et al.  Symbolic time series analysis via wavelet-based partitioning , 2006 .

[5]  David G. Stork,et al.  Pattern Classification , 1973 .

[6]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[7]  D. Ruelle,et al.  Ergodic theory of chaos and strange attractors , 1985 .

[8]  Shalabh Gupta,et al.  Online fatigue damage monitoring by ultrasonic measurements : A symbolic dynamics approach , 2007 .

[9]  K. Mathioudakis,et al.  Gas Turbine Component Fault Identification by Means of Adaptive Performance Modeling , 1990 .

[10]  R. Badii,et al.  Complexity: Hierarchical Structures and Scaling in Physics , 1997 .

[11]  J. Urry.  Complexity , 2006, Interpreting Art.

[12]  Khary I. Parker,et al.  Development of a Turbofan Engine Simulation in a Graphical Simulation Environment , 2001 .

[13]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[14]  Matthew B Kennel,et al.  Estimating good discrete partitions from observed data: symbolic false nearest neighbors. , 2003, Physical review letters.

[15]  Asok Ray,et al.  Symbolic time series analysis via wavelet-based partitioning , 2006, Signal Process..

[16]  A. M. Y. Razak,et al.  An Advanced Model Based Health Monitoring System to Reduce Gas Turbine Ownership Cost , 2000 .

[17]  Asok Ray,et al.  Anomaly Detection in Aircraft Gas Turbine Engines , 2006, J. Aerosp. Comput. Inf. Commun..

[18]  Riti Singh,et al.  Gas Turbine Engine and Sensor Fault Diagnosis Using Optimization Techniques , 2002 .

[19]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[20]  Y. Klimontovich.  Thermodynamics of Chaotic Systems — An introduction , 1994 .

[21]  R. Singh,et al.  Gas turbine engine and sensor fault diagnosis using optimisation techniques , 1999 .

[22]  Sébastien Grondel,et al.  Fatigue crack monitoring of riveted aluminium strap joints by Lamb wave analysis and acoustic emission measurement techniques , 2002 .